Instructions to use hb23/sample_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hb23/sample_data with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hb23/sample_data") prompt = "A photo of <skswr>, studio lighting, standing up" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0367053dcf7c4c5a3d7d18600bc6845fd560b513c6a575338f82e49539e04e44
- Size of remote file:
- 1.4 kB
- SHA256:
- 62be0d59ca9026483b48a8aaf8552fa736a2dc084ffdd91ddff6e110def67d7a
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